Literature DB >> 34695189

The data behind the image-Deep learning and its potential impact in neuro-oncological imaging.

Birgit Ertl-Wagner1,2,3, Farzad Khalvati1,2,3.   

Abstract

Entities:  

Mesh:

Year:  2022        PMID: 34695189      PMCID: PMC8804883          DOI: 10.1093/neuonc/noab249

Source DB:  PubMed          Journal:  Neuro Oncol        ISSN: 1522-8517            Impact factor:   12.300


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  6 in total

1.  Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas.

Authors:  W Han; L Qin; C Bay; X Chen; K-H Yu; N Miskin; A Li; X Xu; G Young
Journal:  AJNR Am J Neuroradiol       Date:  2019-12-19       Impact factor: 3.825

2.  Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients.

Authors:  Zhenyu Tang; Yuyun Xu; Lei Jin; Abudumijiti Aibaidula; Junfeng Lu; Zhicheng Jiao; Jinsong Wu; Han Zhang; Dinggang Shen
Journal:  IEEE Trans Med Imaging       Date:  2020-01-06       Impact factor: 10.048

3.  MRI-Based Deep-Learning Method for Determining Glioma MGMT Promoter Methylation Status.

Authors:  C G B Yogananda; B R Shah; S S Nalawade; G K Murugesan; F F Yu; M C Pinho; B C Wagner; B Mickey; T R Patel; B Fei; A J Madhuranthakam; J A Maldjian
Journal:  AJNR Am J Neuroradiol       Date:  2021-03-04       Impact factor: 3.825

4.  Radiomics of Pediatric Low-Grade Gliomas: Toward a Pretherapeutic Differentiation of BRAF-Mutated and BRAF-Fused Tumors.

Authors:  M W Wagner; N Hainc; F Khalvati; K Namdar; L Figueiredo; M Sheng; S Laughlin; M M Shroff; E Bouffet; U Tabori; C Hawkins; K W Yeom; B B Ertl-Wagner
Journal:  AJNR Am J Neuroradiol       Date:  2021-02-11       Impact factor: 3.825

Review 5.  Radiomics, machine learning, and artificial intelligence-what the neuroradiologist needs to know.

Authors:  Matthias W Wagner; Khashayar Namdar; Asthik Biswas; Suranna Monah; Farzad Khalvati; Birgit B Ertl-Wagner
Journal:  Neuroradiology       Date:  2021-09-18       Impact factor: 2.804

  6 in total

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